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In March 2022, large parts over the north Indian plains including the breadbasket region, and southern Pakistan began experiencing prolonged heat, which continued into May. The event was exacerbated due to prevailing dry conditions in the region, resulting in devastating consequences for public health and agriculture. Using event attribution methods, we analyse the role of human-induced climate change in altering the chances of such an event. To capture the extent of the impacts, we choose March–April average of daily maximum temperature over the most affected region in India and Pakistan as the variable. In observations, the 2022 event has a return period of ∼1-in-100 years. For each of the climate models, we then calculate the change in probability and intensity of a 1-in-100 year event between the actual and counterfactual worlds for quantifying the role of climate change. We estimate that human-caused climate change made this heatwave about 1 °C hotter and 30 times more likely in the current, 2022 climate, as compared to the 1.2 °C cooler, pre-industrial climate. Under a future global warming of 2 °C above pre-industrial levels, heatwaves like this are expected to become even more common (2–20 times more likely) and hotter (by 0 °C–1.5 °C) compared to now. Stronger and frequent heat waves in the future will impact vulnerable groups as conditions in some regions exceed limits for human survivability. Therefore, mitigation is essential for avoiding loss of lives and livelihood. Heat Action Plans have proved effective to help reduce heat-related mortality in both countries.

The emergence of climate change from background variability is a useful metric for identifying changes in local climate which may affect people and ecosystems. Studies have found that equatorial regions, which are typically poorer, experience clearer climate change emergence over the observational record and in model projections. Here, we perform the first analysis of people’s experienced climate change relative to background variability, and we examine where people have already lived through an emergence of local warming. We calculate signal-to-noise (S/N) ratios and combine these with demographic data to compute local emergence of warming over human lifetimes. Younger people have typically experienced less clear emergence of a climate change signal over their lifetimes to date. Over a given time period, tropical, lower-income areas have experienced higher S/N than extratropical and typically higher-income areas. However, this is counter-balanced by the younger populations of these areas such that the median experienced S/N ratio is similar between the wealthiest and poorest parts of the world. Given projected ageing of low-income regions, it is imperative that substantial climate action is taken to avoid local climates becoming unrecognizable within human lifetimes.

The Coupled Model Intercomparison Project Phase 6 (CMIP6) model ensemble projects climate change emerging soonest and most strongly at low latitudes, regardless of the emissions pathway taken. In terms of signal-to-noise (S/N) ratios of average annual temperatures, these models project earlier and stronger emergence under the Shared Socio-economic Pathways than the previous generation did under corresponding Representative Concentration Pathways. Spatial patterns of emergence also change between generations of models; under a high emissions scenario, mid-century S/N is lower than previous studies indicated in Central Africa, South Asia, and parts of South America, West Africa, East Asia, and Western Europe, but higher in most other populated areas. We show that these global and regional changes are caused by a combination of higher effective climate sensitivity in the CMIP6 ensemble, as well as changes to emissions pathways, component-wise effective radiative forcing, and region-scale climate responses between model generations. We also present the first population-weighted calculation of climate change emergence for the CMIP6 ensemble, quantifying the number of people exposed to increasing degrees of abnormal temperatures now and into the future. Our results confirm the expected inequity of climate change-related impacts in the decades between now and the 2050 target for net-zero emissions held by many countries. These findings underscore the importance of concurrent investments in both mitigation and adaptation.

Atmospheric rivers (ARs) are narrow and elongated regions of enhanced horizontal water vapour transport. Considerable research on understanding Northern Hemisphere ARs and their relationship with extreme precipitation has shown that ARs have a strong association with heavy rainfall and flooding. While there has been very little work on ARs in the Southern Hemisphere, global climatologies suggest that ARs are equally as common in both hemispheres. New Zealand in particular is located in a region of high AR frequency. This study aims to test the hypothesis that ARs play a significant role in heavy precipitation and flooding events in New Zealand. We used a recently developed AR identification method and daily station data across New Zealand to test for the concurrence of ARs and extreme rainfall. We found that, at each of the eleven stations analysed, at least seven to all ten of the top ten heaviest precipitation days between 1980 and 2018 were associated with AR conditions. Nine of the ten most damaging floods in New Zealand between 2007 and 2017 occurred during AR events. These results have important implications for understanding extreme rainfall in New Zealand, and ultimately for predicting some of the most hazardous events in the region. This work also highlights that more research on ARs in New Zealand is needed.

New Zealand is one of many higher latitude countries where extreme heat is perceived to be a less consequential impact of climate change, by virtue of its relatively cool climate. Consequently, metrics to quantify the impacts of extreme heat in New Zealand have not kept pace with wider improvements in heatwave definitions. This study evaluates different methods to quantify extreme heat in New Zealand, with a view to improve the knowledge base underpinning future climate change risk assessments. Specifically, this analysis (1) reveals which of New Zealand’s purportedly hottest years in the satellite era are robust to different definitions of extreme heat; (2) introduces a new method of quantifying extreme heat which is applicable across different regions, and serves equally well whether an analysis is contextualised relative to the past (attribution) or for the future (projections); (3) detects previously unidentified heatwaves over recent decades; (4) identifies locally significant increases in extreme heat and the potential lengthening of summer months after only 0.5 °C of global warming; and (5) discusses further research priorities to better understand the impacts of extreme heat in New Zealand over the coming decades.

This study investigates the future changes in dangerous extreme precipitation event in South America, using the multi-model ensemble simulations from the HAPPI experiments. The risks of dangerous extreme precipitation events occurrence, and changes in area and population exposure are quantified. Our results show that the likelihood of dangerous extreme precipitation increases in large parts of South America under future warming; changes in extreme precipitation are nonlinear with increasing global mean temperatures; and exposure plays a minor role compared to hazard. In all the models, limiting warming to 1.5 ° C as opposed to 2 ° C shows a general reduction in both area and population exposure to dangerous extreme precipitation throughout South America. The southeast region of South America exhibited the highest multi-model median percentage of avoided area exposure at 13.3%, while the southwest region shows the lowest percentage at 3.1%. Under all shared socioeconomic pathways, South America Monsoon region and southern South America region yielded the highest multi-model median percentage of avoided population exposure (>10%). The strong spatial heterogeneity in projected changes in all the models highlights the importance of considering location-specific information when designing adaptation measures and investing in disaster preparedness.

Population ageing, an increase in the older age group’s portion of the total population, worsens the heat tolerance of a society. However, impacts of ageing on the social exposure to projected unprecedented hot summers (UHSs) are uncertain. We show that a shifting of the population distribution towards older ages amplifies the vulnerability of a country to the increasing frequency of UHSs as a result of warming during 2040–2070, especially in most populated regions such as China, India, and sub-Saharan countries. The warming scenarios from Representative Concentration Pathway (RCP) 8.5 are combined with population scenarios from three Shared Socio-economic Pathways (SSPs) SSP2, SSP3, and SSP5 together to estimate the exposure to UHSs. The ageing-driven increase in the exposure of elderly to UHSs ranges 51–198, 91–261, and 47–156 million in China, India, and sub-Saharan countries, respectively, between population scenarios. In China, with decreasing total population, the exposure to UHSs will be increased by rapid population ageing. In India and sub-Saharan countries, the potential of ageing to raise the exposure to UHSs will be even larger than that of warming. In contrast, in aged societies with slow ageing trend, e.g. United States and Europe, the warming mainly increases the exposure to UHSs. Our results suggest the changing age structure could exacerbate a country’s heat vulnerability despite limiting warming to a certain level in the future.

Understanding how continuing increases in global mean temperature will exacerbate societal exposure to extreme weather events is a question of profound importance. However, determining population exposure to the impacts of heat extremes at 1.5 °C and 2 °C of global mean warming requires not only (1) a robust understanding of the physical climate system response, but also consideration of (2) projected changes to overall population size, as well as (3) changes to where people will live in the future. This analysis introduces a new framework, adapted from studies of probabilistic event attribution, to disentangle the relative importance of regional climate emergence and changing population dynamics in the exposure to future heat extremes across multiple densely populated regions in Southern Asia and Eastern Africa (SAEA). Our results reveal that, when population is kept at 2015 levels, exposure to heat considered severe in the present decade across SAEA will increase by a factor of 4.1 (2.4–9.6) and 15.8 (5.0–135) under a 1.5°- and 2.0°-warmer world, respectively. Furthermore, projected population changes by the end of the century under an SSP1 and SSP2 scenario can further exacerbate these changes by a factor of 1.2 (1.0–1.3) and 1.5 (1.3–1.7), respectively. However, a large fraction of this additional risk increase is not related to absolute increases in population, but instead attributed to changes in which regions exhibit continued population growth into the future. Further, this added impact of population redistribution will be twice as significant after 2.0 °C of warming, relative to stabilisation at 1.5 °C, due to the non-linearity of increases in heat exposure. Irrespective of the population scenario considered, continued African population expansion will place more people in locations where emergent changes to future heat extremes are exceptionally severe.

The question of whether, how, and to what extent climate change is affecting health is central to many climate and health studies. We describe a set of formal methods, termed detection and attribution, used by climatologists to determine whether a climate trend or extreme event has changed and to estimate the extent to which climate change influenced that change. We discuss events where changing weather patterns were attributed to climate change and extend these analyses to include health impacts from heat waves in 2018 and 2019 in Europe and Japan, and we show how such impact attribution could be applied to melting ice roads in the Arctic. Documenting the causal chain from emissions of greenhouse gases to observed human health outcomes is important input into risk assessments that prioritize health system preparedness and response interventions and into financial investments and communication about potential risk to policy makers and to the public.